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library('rgdal')
library('dismo')
library('raster')
library('sp')
library('maptools')
library('readr')
library('ggplot2')
library('ggmap')
library('sf')
#saving file into a label and reading
occ.data <- read.csv(file = "data/bombus_emu.csv")

summary(occ.data)
            Barcode     
 USNMENT00123087:    1  
 USNMENT00679000:    1  
 USNMENT00679001:    1  
 USNMENT00679002:    1  
 USNMENT00679003:    1  
 USNMENT00679004:    1  
 (Other)        :44108  
                                                                                        Taxon      
 Bombus unidentified : Apidae : Hymenoptera : Insecta : Arthropoda                         :10752  
 Bombus bifarius nearcticus : Apidae : Hymenoptera : Insecta : Arthropoda                  : 2533  
 Bombus (Pyrobombus) mixtus Cresson, 1878 : Apidae : Hymenoptera : Insecta : Arthropoda    : 2122  
 Bombus (Bombus) affinis Cresson : Apidae : Hymenoptera : Insecta : Arthropoda             : 1902  
 Bombus (Pyrobombus) flavifrons Cresson, 1863 : Apidae : Hymenoptera : Insecta : Arthropoda: 1701  
 Bombus (Pyrobombus) bifarius Cresson, 1878 : Apidae : Hymenoptera : Insecta : Arthropoda  : 1553  
 (Other)                                                                                   :23551  
 DarDecimalLatitude DarDecimalLongitude
 Min.   :-53.63     Min.   :-172.72    
 1st Qu.: 39.52     1st Qu.:-122.42    
 Median : 42.08     Median :-105.27    
 Mean   : 43.63     Mean   : -94.24    
 3rd Qu.: 48.09     3rd Qu.: -84.19    
 Max.   : 86.33     Max.   : 175.38    
 NA's   :18110      NA's   :18110      
                      DarScientificName DarScientificNameAuthor
 Bombus unidentified           :10752             :13285       
 Bombus bifarius nearcticus    : 2533   Cresson   :11766       
 Bombus (Pyrobombus) mixtus    : 2122   Smith     : 3193       
 Bombus (Bombus) affinis       : 1902   Nylander  : 2079       
 Bombus (Pyrobombus) flavifrons: 1701   (Linnaeus): 1868       
 Bombus (Pyrobombus) bifarius  : 1553   Greene    : 1781       
 (Other)                       :23551   (Other)   :10142       
 DarScientificNameAuthorYear    DarSpecificEpithet
              :13285         unidentified:10752   
 Cresson, 1878: 4436         bifarius    : 4086   
 Cresson, 1863: 4097         mixtus      : 2122   
 Smith        : 3080         affinis     : 1902   
 Cresson      : 2861         flavifrons  : 1701   
 Nylander     : 2079         melanopygus : 1423   
 (Other)      :14276         (Other)     :22128   
 DarYearCollected       DarCollector  
 Min.   : 947     L. Macior   :15123  
 1st Qu.:1932     [Not Stated]: 4316  
 Median :1965     D. Graham   : 4200  
 Mean   :1953     J. Bridwell : 2096  
 3rd Qu.:1971     E. Stiles   : 1582  
 Max.   :2017     J. Clarke   : 1523  
 NA's   :4567     (Other)     :15274  
                                      AdmGUIDPreferredValue
 ark:/65665/300030cb7-f6da-4dc3-8005-b769a0386842:    1    
 ark:/65665/30003be57-6cf7-4e31-b1e6-df2a59f21999:    1    
 ark:/65665/3000427d0-37c4-4d9e-8e40-494c334429df:    1    
 ark:/65665/30004595f-cd68-4adb-9cb1-50905d58f9e0:    1    
 ark:/65665/30004b971-c32d-44a6-b1e1-c774d8f0da37:    1    
 ark:/65665/3000513bb-73ae-48ed-bfe3-f47fa9a4cf02:    1    
 (Other)                                         :44108    
        DarStateProvince        DarCounty    
 Colorado       : 5326   [Not Stated]:15382  
 Yukon Territory: 3953   Boulder     : 3525  
 Washington     : 3496   Tuolumne    : 2025  
 Sichuan        : 3428   Racine      : 1169  
 California     : 3378   Warren      : 1139  
 [Not Stated]   : 2834   Surrey      :  811  
 (Other)        :21699   (Other)     :20063  
       DarLocality    DarMonthCollected DarDayCollected
 [Not Stated]:14646   Min.   : 1.000    Min.   : 1.0   
 Kluane      : 3771   1st Qu.: 6.000    1st Qu.: 8.0   
 Esher       : 1080   Median : 7.000    Median :15.0   
 Clearview   :  472   Mean   : 6.898    Mean   :15.5   
 Corvallis   :  434   3rd Qu.: 8.000    3rd Qu.:23.0   
 Mount Emei  :  415   Max.   :12.000    Max.   :31.0   
 (Other)     :23296   NA's   :4910      NA's   :7562   
 DarFieldNumber DarCollectorNumber DarPreparations
 Mode:logical   Mode:logical       Pinned:44114   
 NA's:44114     NA's:44114                        
                                                  
                                                  
                                                  
                                                  
                                                  
dir.create(path = 'data')
'data' already exists
dir.create(path = 'output')
'output' already exists
#shows properties of data, rows and columns
dim(occ.data)
[1] 44114    19
#Column names
colnames(occ.data)
 [1] "Barcode"                     "Taxon"                      
 [3] "DarDecimalLatitude"          "DarDecimalLongitude"        
 [5] "DarScientificName"           "DarScientificNameAuthor"    
 [7] "DarScientificNameAuthorYear" "DarSpecificEpithet"         
 [9] "DarYearCollected"            "DarCollector"               
[11] "AdmGUIDPreferredValue"       "DarStateProvince"           
[13] "DarCounty"                   "DarLocality"                
[15] "DarMonthCollected"           "DarDayCollected"            
[17] "DarFieldNumber"              "DarCollectorNumber"         
[19] "DarPreparations"            
#bioclimatic data
bioclim.data<- getData(name = 'worldclim',
                       var = 'bio',
                       res = 2.5,
                       path = "data/")
#disregarding all nulled values for lon and lat
clean.data <- subset(occ.data, !is.na(DarDecimalLatitude) & !is.na(DarDecimalLongitude))
#shows properties of data

dim(clean.data)
[1] 26004    19
clean.data
NA
#Determining what the max and min geographical values of map are
max.lat <- (max(clean.data$DarDecimalLatitude))
min.lat <- (min(clean.data$DarDecimalLatitude))
max.lon <- (max(clean.data$DarDecimalLongitude))
min.lon <- (min(clean.data$DarDecimalLongitude))
#Display Values
max.lat
[1] 86.3264
min.lon
[1] -172.72
max.lon
[1] 175.38
min.lat
[1] -53.63
#Establishing extent of map (will be able to plot only north american in later model)
geo.extent <- extent(x = c(min.lon, max.lon, min.lat, max.lat))
c(min.lon, max.lon, min.lat, max.lat)
#Using only collumns from lon and lat
loc.data <- clean.data [, c('DarDecimalLongitude', 'DarDecimalLatitude')]
loc.data
NA
data("wrld_simpl")

plot(wrld_simpl,
     xlim = c(min.lon, max.lon),
     ylim = c(min.lat, max.lat ), 
     axes = TRUE,
     col = 'grey95')

#adding points on to map
points(x = loc.data$DarDecimalLongitude,
       y = loc.data$DarDecimalLatitude,
       col = 'olivedrab',
       pch = 20,
       cex = 0.75)


#box around map
box()

bc.model <- bioclim(x = bioclim.data, p = loc.data)

predict.presence <- dismo::predict(object = bc.model, x = bioclim.data, ext = geo.extent)
#plot base map
plot(wrld_simpl,
     xlim = c(min.lon, max.lon),
     ylim = c(min.lat, max.lat),
     axes = TRUE,
     col = "grey95")

#adding model probabilities
plot(predict.presence, add = TRUE)
 
#redrawing country borders
plot(wrld_simpl, add = TRUE, border = "grey6")

#adding OG observations
points(x = loc.data$DarDecimalLongitude,
       y = loc.data$DarDecimalLatitude,
       col = 'olivedrab',
       pch = 20,
       cex = 0.75)

NA
NA
sep.data <- clean.data [, c('Taxon', 'DarDecimalLongitude', 'DarDecimalLatitude')]
pyro.data <- read.csv(file = 'data/Pyrobombus_emu.csv')

locpyro.data <- pyro.data [, c('DarDecimalLongitude', 'DarDecimalLatitude')]

locpyro.data
plot(wrld_simpl,
     xlim = c(min.lon, max.lon),
     ylim = c(min.lat, max.lat),
     axes = TRUE,
     col = "grey95")

plot(wrld_simpl, add = TRUE, border = "grey5")

points(x = clean.data$DarDecimalLongitude,
       y = clean.data$DarDecimalLatitude,
       col = 'olivedrab',
       pch = 20,
       cex = 0.75)

points(x = locpyro.data$DarDecimalLongitude,
       y = locpyro.data$DarDecimalLatitude,
       col = 'red',
       pch = 20,
       cex = 0.75)
Agro.data <- read.csv(file = 'data/Agro_emu.csv')
Alpigeno.data <- read.csv(file = 'data/Alpigeno_emu.csv')
Alpino.data <- read.csv(file = 'data/Alpino_emu.csv')
Bombias.data <- read.csv(file = 'data/Bombias_emu.csv')
BombusSub.data <- read.csv(file = 'data/BombusSub_emu.csv')
Brachy.data <- read.csv (file = 'data/Brachycephalobombus_emu.csv')
Coccineo.data <- read.csv(file = 'data/Coccineo_emu.csv')
Confusii.data <- read.csv(file = 'data/Confusii_emu.csv')
Crotchii.data <- read.csv(file = 'data/Crotchii_emu.csv')
Collumano.data <- read.csv(file = 'data/Collumano_emu.csv')
Diverso.data <- read.csv(file = 'data/Diverso_emu.csv')
Dasy.data <- read.csv(file = 'data/Dasy_emu.csv')
Fervido.data <- read.csv(file = 'data/Fervido_emu.csv')
Thoraco.data <- read.csv(file = 'data/Thoraco_emu.csv')
#Disregarding all na values in each data frame
Alpigeno.data <- subset(Alpigeno.data, !is.na(DarLatitude) & !is.na(DarLongitude))
locAlpigen.data <- Alpigeno.data [, c('DarLongitude', 'DarLatitude')]
locAlpigen.data

Agro.data <- subset(Agro.data, !is.na(DarLatitude) & !is.na(DarLongitude))
locAgro.data <- Agro.data [, c('DarLongitude', 'DarLatitude')]

Alpino.data <- subset(Alpino.data, !is.na(DarLatitude) & !is.na(DarLongitude))
locAlpino.data <- Alpino.data [, c('DarLongitude', 'DarLatitude')]

Bombias.data <- subset(Bombias.data, !is.na(DarLatitude) & !is.na(DarLongitude))
locBombias.data <- Bombias.data [, c('DarLongitude', 'DarLatitude')]


BombusSub.data <- subset(BombusSub.data, !is.na(DarLatitude) & !is.na(DarLongitude))
locBombus.data <- BombusSub.data [, c('DarLongitude', 'DarLatitude')]


Brachy.data <- subset(Brachy.data, !is.na(DarLatitude) & !is.na(DarLongitude))
locBrachy.data <- Brachy.data [, c('DarLongitude', 'DarLatitude')]

#skipped Coccineo

Collum.data <- subset(Collumano.data, !is.na(DarLatitude) & !is.na(DarLongitude))
locCollum.data <- Collum.data [, c('DarLongitude', 'DarLatitude')]

Thoraco.data <- subset(Thoraco.data, !is.na(DarLatitude) & !is.na(DarLongitude))
locThoraco.data <- Thoraco.data [, c('DarLongitude', 'DarLatitude')]
plot(wrld_simpl,
     xlim = c(min.lon, max.lon),
     ylim = c(min.lat, max.lat),
     axes = TRUE,
     col = "grey95")

plot(wrld_simpl, add = TRUE, border = "grey5")

#points(x = clean.data$DarDecimalLongitude,
 #      y = clean.data$DarDecimalLatitude,
  #     col = 'olivedrab',
   #    pch = 20,
    #   cex = 0.75)

points(x = locpyro.data$DarDecimalLongitude,
       y = locpyro.data$DarDecimalLatitude,
       col = 'red',
       pch = 20,
       cex = 0.75)

points(x = locAlpigen.data$DarLongitude,
       y = locAlpigen.data$DarLatitude,
       col = 'yellow',
       pch = 20,
       cex = 0.75)

points(x = locAgro.data$DarLongitude,
       y = locAgro.data$DarLatitude,
       col = 'pink',
       pch = 20,
       cex = 0.75)
 
points(x = locAlpino.data$DarLongitude,
       y = locAlpino.data$DarLatitude,
       col = 'blue',
       pch = 20,
       cex = 0.75)


points(x = locBombias.data$DarLongitude,
       y = locBombias.data$DarLatitude,
       col = 'purple',
       pch = 20,
       cex = 0.75)

points(x = locBombus.data$DarLongitude,
       y = locBombus.data$DarLatitude,
       col = 'grey',
       pch = 20,
       cex = 0.75)

points(x = locBrachy.data$DarLongitude,
       y = locBrachy.data$DarLatitude,
       col = 'olivedrab',
       pch = 20,
       cex = 0.75)

points(x = locCollum.data$DarLongitude,
       y = locCollum.data$DarLatitude,
       col = 'black',
       pch = 20,
       cex = 0.75)

points(x = locThoraco.data$DarLongitude,
       y = locThoraco.data$DarLatitude,
       col = 'green',
       pch = 20,
       cex = 0.75)

plot(wrld_simpl,
     xlim = c(-170, -39),
     ylim = c(11, 72),
     axes = TRUE,
     col = "grey95")
points(x = locpyro.data$DarDecimalLongitude,
       y = locpyro.data$DarDecimalLatitude,
       col = 'red',
       pch = 20,
       cex = 0.75)

points(x = locBombus.data$DarLongitude,
       y = locBombus.data$DarLatitude,
       col = 'grey',
       pch = 20,
       cex = 0.75)
points(x = locCollum.data$DarLongitude,
       y = locCollum.data$DarLatitude,
       col = 'black',
       pch = 20,
       cex = 0.75)

points(x = locThoraco.data$DarLongitude,
       y = locThoraco.data$DarLatitude,
       col = 'green',
       pch = 20,
       cex = 0.75) 

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---
title: "Distribution Map: Bombus and its Subgenera"
output:
  word_document: default
  pdf_document: default
  html_notebook: default
---

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```{r}
library('rgdal')
library('dismo')
library('raster')
library('sp')
library('maptools')
library('readr')
library('ggplot2')
library('ggmap')
library('sf')

```


```{r}
#saving file into a label and reading
occ.data <- read.csv(file = "data/bombus_emu.csv")

summary(occ.data)
```

```{r}
dir.create(path = 'data')
dir.create(path = 'output')

```


```{r}
#shows properties of data, rows and columns
dim(occ.data)
#Column names
colnames(occ.data)

#bioclimatic data
bioclim.data<- getData(name = 'worldclim',
                       var = 'bio',
                       res = 2.5,
                       path = "data/")
```


```{r}
#disregarding all nulled values for lon and lat
clean.data <- subset(occ.data, !is.na(DarDecimalLatitude) & !is.na(DarDecimalLongitude))


```
```{r}

dim(clean.data)
clean.data

```






```{r}
#Determining what the max and min geographical values of map are
max.lat <- (max(clean.data$DarDecimalLatitude))
min.lat <- (min(clean.data$DarDecimalLatitude))
max.lon <- (max(clean.data$DarDecimalLongitude))
min.lon <- (min(clean.data$DarDecimalLongitude))
```
```{r}
#Display Values
max.lat
min.lon
max.lon
min.lat



```
```{r}
#Establishing extent of map (will be able to plot only north american in later model)
geo.extent <- extent(x = c(min.lon, max.lon, min.lat, max.lat))
c(min.lon, max.lon, min.lat, max.lat)
```


```{r}
#Using only collumns from lon and lat
loc.data <- clean.data [, c('DarDecimalLongitude', 'DarDecimalLatitude')]
loc.data

```

```{r}
data("wrld_simpl")

plot(wrld_simpl,
     xlim = c(min.lon, max.lon),
     ylim = c(min.lat, max.lat ), 
     axes = TRUE,
     col = 'grey95')

#adding points on to map
points(x = loc.data$DarDecimalLongitude,
       y = loc.data$DarDecimalLatitude,
       col = 'olivedrab',
       pch = 20,
       cex = 0.75)


#box around map
box()
```


```{r}
bc.model <- bioclim(x = bioclim.data, p = loc.data)

predict.presence <- dismo::predict(object = bc.model, x = bioclim.data, ext = geo.extent)
```
```{r}
#plot base map
plot(wrld_simpl,
     xlim = c(min.lon, max.lon),
     ylim = c(min.lat, max.lat),
     axes = TRUE,
     col = "grey95")

#adding model probabilities
plot(predict.presence, add = TRUE)
 
#redrawing country borders
plot(wrld_simpl, add = TRUE, border = "grey6")

#adding OG observations
points(x = loc.data$DarDecimalLongitude,
       y = loc.data$DarDecimalLatitude,
       col = 'olivedrab',
       pch = 20,
       cex = 0.75)
```
```{r}
sep.data <- clean.data [, c('Taxon', 'DarDecimalLongitude', 'DarDecimalLatitude')]

```
```{r}
pyro.data <- read.csv(file = 'data/Pyrobombus_emu.csv')

locpyro.data <- pyro.data [, c('DarDecimalLongitude', 'DarDecimalLatitude')]

locpyro.data
```
```{r}
plot(wrld_simpl,
     xlim = c(min.lon, max.lon),
     ylim = c(min.lat, max.lat),
     axes = TRUE,
     col = "grey95")

plot(wrld_simpl, add = TRUE, border = "grey5")

points(x = clean.data$DarDecimalLongitude,
       y = clean.data$DarDecimalLatitude,
       col = 'olivedrab',
       pch = 20,
       cex = 0.75)

points(x = locpyro.data$DarDecimalLongitude,
       y = locpyro.data$DarDecimalLatitude,
       col = 'red',
       pch = 20,
       cex = 0.75)
```
```{r}
Agro.data <- read.csv(file = 'data/Agro_emu.csv')
Alpigeno.data <- read.csv(file = 'data/Alpigeno_emu.csv')
Alpino.data <- read.csv(file = 'data/Alpino_emu.csv')
Bombias.data <- read.csv(file = 'data/Bombias_emu.csv')
BombusSub.data <- read.csv(file = 'data/BombusSub_emu.csv')
Brachy.data <- read.csv (file = 'data/Brachycephalobombus_emu.csv')
Coccineo.data <- read.csv(file = 'data/Coccineo_emu.csv')
Confusii.data <- read.csv(file = 'data/Confusii_emu.csv')
Crotchii.data <- read.csv(file = 'data/Crotchii_emu.csv')
Collumano.data <- read.csv(file = 'data/Collumano_emu.csv')
Diverso.data <- read.csv(file = 'data/Diverso_emu.csv')
Dasy.data <- read.csv(file = 'data/Dasy_emu.csv')
Fervido.data <- read.csv(file = 'data/Fervido_emu.csv')
Thoraco.data <- read.csv(file = 'data/Thoraco_emu.csv')
```
```{r}
#Disregarding all na values in each data frame
Alpigeno.data <- subset(Alpigeno.data, !is.na(DarLatitude) & !is.na(DarLongitude))
locAlpigen.data <- Alpigeno.data [, c('DarLongitude', 'DarLatitude')]
locAlpigen.data

Agro.data <- subset(Agro.data, !is.na(DarLatitude) & !is.na(DarLongitude))
locAgro.data <- Agro.data [, c('DarLongitude', 'DarLatitude')]

Alpino.data <- subset(Alpino.data, !is.na(DarLatitude) & !is.na(DarLongitude))
locAlpino.data <- Alpino.data [, c('DarLongitude', 'DarLatitude')]

Bombias.data <- subset(Bombias.data, !is.na(DarLatitude) & !is.na(DarLongitude))
locBombias.data <- Bombias.data [, c('DarLongitude', 'DarLatitude')]


BombusSub.data <- subset(BombusSub.data, !is.na(DarLatitude) & !is.na(DarLongitude))
locBombus.data <- BombusSub.data [, c('DarLongitude', 'DarLatitude')]


Brachy.data <- subset(Brachy.data, !is.na(DarLatitude) & !is.na(DarLongitude))
locBrachy.data <- Brachy.data [, c('DarLongitude', 'DarLatitude')]

#skipped Coccineo

Collum.data <- subset(Collumano.data, !is.na(DarLatitude) & !is.na(DarLongitude))
locCollum.data <- Collum.data [, c('DarLongitude', 'DarLatitude')]

Thoraco.data <- subset(Thoraco.data, !is.na(DarLatitude) & !is.na(DarLongitude))
locThoraco.data <- Thoraco.data [, c('DarLongitude', 'DarLatitude')]
```
```{r}
plot(wrld_simpl,
     xlim = c(min.lon, max.lon),
     ylim = c(min.lat, max.lat),
     axes = TRUE,
     col = "grey95")

plot(wrld_simpl, add = TRUE, border = "grey5")

#points(x = clean.data$DarDecimalLongitude,
 #      y = clean.data$DarDecimalLatitude,
  #     col = 'olivedrab',
   #    pch = 20,
    #   cex = 0.75)

points(x = locpyro.data$DarDecimalLongitude,
       y = locpyro.data$DarDecimalLatitude,
       col = 'red',
       pch = 20,
       cex = 0.75)

points(x = locAlpigen.data$DarLongitude,
       y = locAlpigen.data$DarLatitude,
       col = 'yellow',
       pch = 20,
       cex = 0.75)

points(x = locAgro.data$DarLongitude,
       y = locAgro.data$DarLatitude,
       col = 'pink',
       pch = 20,
       cex = 0.75)
 
points(x = locAlpino.data$DarLongitude,
       y = locAlpino.data$DarLatitude,
       col = 'blue',
       pch = 20,
       cex = 0.75)


points(x = locBombias.data$DarLongitude,
       y = locBombias.data$DarLatitude,
       col = 'purple',
       pch = 20,
       cex = 0.75)

points(x = locBombus.data$DarLongitude,
       y = locBombus.data$DarLatitude,
       col = 'grey',
       pch = 20,
       cex = 0.75)

points(x = locBrachy.data$DarLongitude,
       y = locBrachy.data$DarLatitude,
       col = 'olivedrab',
       pch = 20,
       cex = 0.75)

points(x = locCollum.data$DarLongitude,
       y = locCollum.data$DarLatitude,
       col = 'black',
       pch = 20,
       cex = 0.75)

points(x = locThoraco.data$DarLongitude,
       y = locThoraco.data$DarLatitude,
       col = 'green',
       pch = 20,
       cex = 0.75)
```
```{r}
plot(wrld_simpl,
     xlim = c(-170, -39),
     ylim = c(11, 72),
     axes = TRUE,
     col = "grey95")
points(x = locpyro.data$DarDecimalLongitude,
       y = locpyro.data$DarDecimalLatitude,
       col = 'red',
       pch = 20,
       cex = 0.75)

points(x = locBombus.data$DarLongitude,
       y = locBombus.data$DarLatitude,
       col = 'grey',
       pch = 20,
       cex = 0.75)
points(x = locCollum.data$DarLongitude,
       y = locCollum.data$DarLatitude,
       col = 'black',
       pch = 20,
       cex = 0.75)

points(x = locThoraco.data$DarLongitude,
       y = locThoraco.data$DarLatitude,
       col = 'green',
       pch = 20,
       cex = 0.75) 
```

Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Cmd+Option+I*.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Cmd+Shift+K* to preview the HTML file). 

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.

